Wearable devices are being introduced by various companies and are becoming more popular in what the wearable devices can do. One example of a wearable device is a head-mounted video device, such as, Google Glass®.
A critical capability with wearable devices, such as the head-mounted video device, is detecting a region of interest in a scene in real-time as a given activity is proceeding. As the population moves from traditional environmental cameras to mobile and wearable cameras, it becomes important to consider not only the accuracy of the method, but also the power and computing resource usage since the wearable devices may have very limited processing and computing resources. For example, the wearable devices are much smaller than traditional laptop computers and desktop computers and do not have room to accommodate high powered processors and a large amount of memory.
Some current methods that are used to detect a region of interest use anticipated shapes to detect a hand gesture. For example, the method may look to see if the image contains any shapes that match a predefined library of shapes. However, if the shape is not in the predefined library then the region of interest may not be detected. Moreover, such methods are computationally expensive due to the cost of sliding-window-based template matching, which is typically used, and therefore, are not suitable for wearable computing where power consumption is of critical concern.